plot.missSBM_fit

Plot function for the various fields of a <code>missSBM_fit</code>: the fitted
SBM (network or connectivity), and a plot monitoring the optimization.

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0
due to missing information between node pairs), it is possible to account for the underlying process
that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
adjusts the popular stochastic block model from network data sampled under various missing data conditions,
as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Julien Chiquet

missSBM

Handling Missing Data in Stochastic Block Models

Pierre Barbillon

Timothée Tabouy

Jean-Benoist Léger

François Gindraud

großBM team 

plot.missSBM_fit function

<dl><dt>x</dt>
<dd>an object with class <code>missSBM_fit</code></dd>
<dt>type</dt>
<dd>the type specifies the field to plot, either "imputed", "expected", "meso", or "monitoring"</dd>
<dt>dimLabels</dt>
<dd>: a list of two characters specifying the labels of the nodes. Default to <code>list(row= 'node',col = 'node')</code>)</dd>
<dt>...</dt>
<dd>additional parameters for S3 compatibility. Not used</dd></dl>

Arguments

Visualization for an object missSBM_fit — plot.missSBM_fit

<dl>

<dt>x</dt>
<dd>an object with class <code>missSBM_fit</code></dd>


<dt>type</dt>
<dd>the type specifies the field to plot, either "imputed", "expected", "meso", or "monitoring"</dd>


<dt>dimLabels</dt>
<dd>: a list of two characters specifying the labels of the nodes. Default to <code>list(row= 'node',col = 'node')</code>)</dd>


<dt>...</dt>
<dd>additional parameters for S3 compatibility. Not used</dd>

</dl>

plot.missSBM_fit: Visualization for an object `missSBM_fit`

Description

Usage

Value

Arguments